Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images Article Swipe
Piotr Sobecki
,
Rafał Jóźwiak
,
Katarzyna Sklinda
,
Artur Przelaskowski
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7717/peerj.11006
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7717/peerj.11006
Background Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations. Methods A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes. Results The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. Conclusions The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.
Related Topics
Concepts
Computer science
Convolutional neural network
Prostate cancer
Parametric statistics
Artificial intelligence
Domain knowledge
Encoding (memory)
Parametric model
Convolution (computer science)
Deep learning
Artificial neural network
Pattern recognition (psychology)
Cancer
Medicine
Internal medicine
Mathematics
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj.11006
- OA Status
- gold
- Cited By
- 13
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3134177550
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3134177550Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj.11006Digital Object Identifier
- Title
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Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI imagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-09Full publication date if available
- Authors
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Piotr Sobecki, Rafał Jóźwiak, Katarzyna Sklinda, Artur PrzelaskowskiList of authors in order
- Landing page
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https://doi.org/10.7717/peerj.11006Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.7717/peerj.11006Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Prostate cancer, Parametric statistics, Artificial intelligence, Domain knowledge, Encoding (memory), Parametric model, Convolution (computer science), Deep learning, Artificial neural network, Pattern recognition (psychology), Cancer, Medicine, Internal medicine, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 7, 2022: 1Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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